Before removing outliers
ggplot(d, aes(ReactionTime, fill=Task)) +
geom_density(alpha = .5)
summary(d$ReactionTime)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 735 913 1111 1212 42181
Long tail justifies outlier removal?
ggplot(d, aes(LogReactionTime, fill=Task)) +
geom_density(alpha = .5)
summary(d$LogReactionTime)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6931 6.5999 6.8167 6.8731 7.1000 10.6497
agr = d %>%
group_by(Task,LogReactionTime) %>%
summarize(MeanCorrectedAccuracy = mean(CorrectedAccuracy) )
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x = MeanCorrectedAccuracy, y = LogReactionTime, fill = MeanCorrectedAccuracy)) +
geom_boxplot(alpha = 0.7) + # Boxplot
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
facet_wrap(~Task) +
labs(title = "Reaction Time by CorrectedAccuracy",
x = "CorrectedAccuracy",
y = "Reaction Time (ms)") +
theme(legend.position = "none") # Remove legend
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(d, aes(x = CorrectedAccuracy, y = LogReactionTime, fill = Task)) +
geom_violin(alpha = 0.7) + # Violin plot
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
labs(title = "Reaction Time by CorrectedAccuracy",
x = "CorrectedAccuracy",
y = "Reaction Time (ms)")
# theme(legend.position = "none") # Remove legend
agr <- d %>%
group_by(Task) %>%
reframe(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
agr <- d %>%
group_by(Task,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr <- d %>%
group_by(BlockOrder,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr <- d %>%
group_by(Task,ID.true) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
length(unique(d$ID.true))
## [1] 40
inacc.parts <- d %>%
group_by(ID.true) %>%
summarise(MeanCorrectedAccuracy = mean(CorrectedAccuracy)) %>%
filter(MeanCorrectedAccuracy < .75)
# How many participants have accuracy < .75?
length(unique(inacc.parts$ID.true))
## [1] 8
d.inaccurate.removed <- d %>%
anti_join(inacc.parts, by = "ID.true")
# Sanity check
length(unique(d.inaccurate.removed$ID.true))
## [1] 32
agr <- d.inaccurate.removed %>%
group_by(Task) %>%
reframe(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
agr <- d.inaccurate.removed %>%
group_by(Task,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
agr <- d.inaccurate.removed %>%
group_by(Task,ID.true) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Remove subjects with ReactionTime higher than 3x IQR
summary(d.inaccurate.removed$LogReactionTime)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.100 6.639 6.855 6.941 7.146 9.221
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 6.924 7.328 7.436 7.479 7.579 10.008
range(d.inaccurate.removed$LogReactionTime)
## [1] 6.100319 9.220588
hist(d.inaccurate.removed$LogReactionTime, breaks=100, col="lightblue", xlab="LogReactionTime (ms)",
main="Histogram with Normal Curve")
quantile(d.inaccurate.removed$LogReactionTime)
## 0% 25% 50% 75% 100%
## 6.100319 6.638568 6.854882 7.145984 9.220588
IQR(d.inaccurate.removed$LogReactionTime)*3 # 0.7526289
## [1] 1.52225
cutoff.high <- quantile(d.inaccurate.removed$LogReactionTime)[4] + IQR(d.inaccurate.removed$LogReactionTime)*3 # 8.419261
cutoff.low <- quantile(d.inaccurate.removed$LogReactionTime)[2] - IQR(d.inaccurate.removed$LogReactionTime)*3# 6.5088838.419261
# remove subjects with ReactionTime higher than 3 x IQR
df.outliers.removed <- subset(d.inaccurate.removed, (d.inaccurate.removed$LogReactionTime > cutoff.low) & (d.inaccurate.removed$LogReactionTime < cutoff.high))
hist(df.outliers.removed$LogReactionTime, col="lightblue", xlab="LogReactionTime (ms)",
main="Histogram with Normal Curve")
agr = df.outliers.removed %>%
group_by(Task, LogReactionTime) %>%
summarize(MeanCorrectedAccuracy = mean(CorrectedAccuracy))
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x = MeanCorrectedAccuracy, y = LogReactionTime, fill = MeanCorrectedAccuracy)) +
geom_boxplot(alpha = 0.7) + # Boxplot
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
facet_wrap(~Task) +
labs(title = "Reaction Time by CorrectedAccuracy",
x = "CorrectedAccuracy",
y = "Reaction Time (ms)") +
theme(legend.position = "none") # Remove legend
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(df.outliers.removed, aes(x = CorrectedAccuracy, y = LogReactionTime, fill = Task)) +
geom_violin(alpha = 0.7) + # Violin plot
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
labs(title = "Reaction Time by CorrectedAccuracy",
x = "CorrectedAccuracy",
y = "Reaction Time (ms)")
agr <- df.outliers.removed %>%
group_by(Task) %>%
reframe(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
# View(agr)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
# guides(fill = "none")
agr <- df.outliers.removed %>%
# filter(PennElementType == "Selector") %>%
# select(ID.true,Word,CorrectedAccuracy) %>%
group_by(Task,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
guides(fill = "none")
# View(d[(d$ID.true == c("56cc78e3ccc0e20006b82a7d")) & (d$Word == c("envy")),])
agr <- df.outliers.removed %>%
group_by(BlockOrder,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
# View(d[(d$ID.true == c("56cc78e3ccc0e20006b82a7d")) & (d$Word == c("envy")),])
agr = df.outliers.removed %>%
group_by(Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime),
CILow = ci.low(ReactionTime),
CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow,
YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
geom_density(alpha = .4)
ggplot(agr, aes(x=Task, y=MeanReactionTime,fill=Task)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
guides(fill = "none")
agr = df.outliers.removed %>%
group_by(BlockOrder,Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime),
CILow = ci.low(ReactionTime),
CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow,
YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'BlockOrder', 'Task'. You can override
## using the `.groups` argument.
ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
geom_density(alpha = .4)
ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
facet_wrap(~BlockOrder) +
geom_density(alpha = .4)
ggplot(agr, aes(x=Task, y=MeanReactionTime,fill=BlockOrder)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_point(color = "black", size = 1.5, alpha = 0.5) # Centered points
agr = df.outliers.removed %>%
group_by(Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanReactionTime,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr = df.outliers.removed %>%
group_by(BlockOrder,Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'BlockOrder', 'Task'. You can override
## using the `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word,ncol=5) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr = df.outliers.removed %>%
group_by(Task,ConcValCombo) %>%
reframe(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=ConcValCombo)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr = df.outliers.removed %>%
group_by(BlockOrder,Task,ConcValCombo) %>%
reframe(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
i actually don’t think this is the right call if we want to look at within-subjects behavior
inacc.parts.group <- d %>%
group_by(Task,ID.true) %>%
summarise(MeanCorrectedAccuracy = mean(CorrectedAccuracy)) %>%
filter(MeanCorrectedAccuracy < .75)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
# How many participants have accuracy < .75?
length(unique(inacc.parts.group$ID.true))
## [1] 11
d.inaccurate.removed.group <- d %>%
anti_join(inacc.parts.group, by = "ID.true")
# Sanity check
length(unique(d.inaccurate.removed.group$ID.true))
## [1] 29
agr <- d.inaccurate.removed.group %>%
group_by(Task) %>%
reframe(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
agr <- d.inaccurate.removed.group %>%
group_by(Task,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
agr <- d.inaccurate.removed.group %>%
group_by(Task,ID.true) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Remove subjects with ReactionTime higher than 3x IQR
summary(d.inaccurate.removed.group$LogReactionTime)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.100 6.637 6.849 6.934 7.130 9.221
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 6.924 7.328 7.436 7.479 7.579 10.008
range(d.inaccurate.removed.group$LogReactionTime)
## [1] 6.100319 9.220588
hist(d.inaccurate.removed.group$LogReactionTime, breaks=100, col="lightblue", xlab="LogReactionTime (ms)",
main="Histogram with Normal Curve")
quantile(d.inaccurate.removed.group$LogReactionTime)
## 0% 25% 50% 75% 100%
## 6.100319 6.636930 6.849066 7.130299 9.220588
IQR(d.inaccurate.removed.group$LogReactionTime)*3 # 0.7526289
## [1] 1.480105
cutoff.high <- quantile(d.inaccurate.removed.group$LogReactionTime)[4] + IQR(d.inaccurate.removed.group$LogReactionTime)*3 # 8.419261
cutoff.low <- quantile(d.inaccurate.removed.group$LogReactionTime)[2] - IQR(d.inaccurate.removed.group$LogReactionTime)*3# 6.5088838.419261
# remove subjects with ReactionTime higher than 3 x IQR
df.outliers.removed.group <- subset(d.inaccurate.removed.group, (d.inaccurate.removed.group$LogReactionTime > cutoff.low) & (d.inaccurate.removed.group$LogReactionTime < cutoff.high))
hist(df.outliers.removed.group$LogReactionTime, col="lightblue", xlab="LogReactionTime (ms)",
main="Histogram with Normal Curve")
agr = df.outliers.removed.group %>%
group_by(Task,LogReactionTime) %>%
summarize(MeanCorrectedAccuracy = mean(CorrectedAccuracy) )
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x = MeanCorrectedAccuracy, y = LogReactionTime, fill = MeanCorrectedAccuracy)) +
geom_boxplot(alpha = 0.7) + # Boxplot
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
facet_wrap(~Task) +
labs(title = "Reaction Time by CorrectedAccuracy",
x = "CorrectedAccuracy",
y = "Reaction Time (ms)") +
theme(legend.position = "none") # Remove legend
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(df.outliers.removed.group, aes(x = CorrectedAccuracy, y = LogReactionTime, fill = Task)) +
geom_violin(alpha = 0.7) + # Violin plot
geom_jitter(position = position_jitter(0.2), color = "black", size = 1.5, alpha = 0.5) + # Add jittered points
labs(title = "Reaction Time by CorrectedAccuracy",
x = "CorrectedAccuracy",
y = "Reaction Time (ms)")
# theme(legend.position = "none") # Remove legend
agr <- df.outliers.removed.group %>%
group_by(Task) %>%
reframe(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
# View(agr)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
# guides(fill = "none")
agr <- df.outliers.removed.group %>%
# filter(PennElementType == "Selector") %>%
# select(ID.true,Word,CorrectedAccuracy) %>%
group_by(Task,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
guides(fill = "none")
# View(d[(d$ID.true == c("56cc78e3ccc0e20006b82a7d")) & (d$Word == c("envy")),])
agr <- df.outliers.removed.group %>%
group_by(BlockOrder,Word) %>%
mutate(MeanCorrectedAccuracy = mean(CorrectedAccuracy),
CILow = ci.low(CorrectedAccuracy),
CIHigh = ci.high(CorrectedAccuracy)) %>%
mutate(YMin = MeanCorrectedAccuracy - CILow,
YMax = MeanCorrectedAccuracy + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanCorrectedAccuracy,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
# View(d[(d$ID.true == c("56cc78e3ccc0e20006b82a7d")) & (d$Word == c("envy")),])
agr = df.outliers.removed.group %>%
group_by(Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime),
CILow = ci.low(ReactionTime),
CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow,
YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
geom_density(alpha = .4)
ggplot(agr, aes(x=Task, y=MeanReactionTime,fill=Task)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_point(color = "black", size = 1.5, alpha = 0.5) # Centered points
agr = df.outliers.removed.group %>%
group_by(BlockOrder,Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime),
CILow = ci.low(ReactionTime),
CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow,
YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'BlockOrder', 'Task'. You can override
## using the `.groups` argument.
ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
geom_density(alpha = .4)
ggplot(agr, aes(x=MeanReactionTime, fill=Task)) +
facet_wrap(~BlockOrder) +
geom_density(alpha = .4)
ggplot(agr, aes(x=Task, y=MeanReactionTime,fill=BlockOrder)) +
geom_violin(trim=FALSE,alpha=.4) +
geom_point(color = "black", size = 1.5, alpha = 0.5)
agr = df.outliers.removed.group %>%
group_by(Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Word,y=MeanReactionTime,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr = df.outliers.removed.group %>%
group_by(BlockOrder,Task,Word) %>%
summarize(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
## `summarise()` has grouped output by 'BlockOrder', 'Task'. You can override
## using the `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word,ncol=5) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# guides(fill = "none")
agr = df.outliers.removed.group %>%
group_by(Task,ConcValCombo) %>%
reframe(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=ConcValCombo)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
agr = df.outliers.removed.group %>%
group_by(BlockOrder,Task,ConcValCombo) %>%
reframe(MeanReactionTime = mean(ReactionTime), CILow = ci.low(ReactionTime), CIHigh = ci.high(ReactionTime)) %>%
mutate(YMin = MeanReactionTime - CILow, YMax = MeanReactionTime + CIHigh)
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanReactionTime,fill=BlockOrder)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))